Detection Quality in Multimedia Wireless Sensor Networks with Travelers' Favorite Paths
The performance of a surveillance wireless sensor network is generally measured
with its detection capability which is affected by various parameters such as the sensor
count, the sensor range, the area width and the target mobility model. We assume that
intruders prefer some favorite paths because of their geographical advantages and pass
through them instead of following a random mobility model. These paths are generally
in close vicinity of each other and they can be bounded in a region.
In this thesis, we inspect the travelers favorite region notions and propose some
image processing tools to detect their location within a border area. Following this,
we present a closed form of the detection probability as the detection quality measure
in the existence of travelers favorite paths. The detection probability is reduced to
the geometric line intersection problem using bijection and the boundary conditions
of intruder trajectories for the border area and the favorite regions are determined.
The line intersection problem is solved using tools from the integral geometry and geometric
probability. The effect of the favorable region on the detection quality under
different conditions is calculated using probabilistic models. The accuracy of the proposed
quality measure is validated by both analytical methods and simulation results.
Furthermore, the importance of the intrusion model on the network performance is
presented using realistic scenarios. It is shown that the existence of favorite paths has
significant impact on the detection quality of the network. We extend our work to
border areas with multiple favorite path regions and present a closed form of detection
probability for such generic cases. We also inspect the effects of various system parameters
such as the sensing model and application scenarios on the detection quality
measure using both analytical tools and simulations. The proposed detection quality
measure provides analytical tools to forecast the expected detection performance and
to optimize the network according to the intruder mobility model.